Model Predictive Current Control With Model-Aid Extended State Observer Compensation for PMSM Drive

Abstract

Model predictive current controller is a popular and effective technique to provide fast dynamic response in the field of motor control. However, conventional predictive controllers are susceptible to deteriorating control performance when model mismatch exists, such as changes in motor parameters due to the temperature variations. Therefore, this article proposes a precise model-aid extended state observer (MAESO) compensation-based real-time model predictive current controller with enhanced parameter robustness performance and high bandwidth. The predictive controller is converted into the form of multiparameter quadratic programming for online solution using numerical computational method and the constraints are linearized. In addition, the disturbances estimated by MAESO are fed back to the controller in the form of parameters for cycle-by-cycle compensation without extra controller design. Comparative simulations and experiments under different operating conditions are carried out to verify the effectiveness and superiority of the proposed method. Index Terms-Extended state observer (ESO), model predictive control, permanent magnet synchronous motor (PMSM) drive, quadratic programming. I. INTRODUCTION F OR many years, permanent magnet synchronous motors (PMSM) have received continuous attention [1], [2], [3], [4] due to their high torque density, efficiency, and simple structure. They are widely used in almost all industrial fields, such as manipulator joints [5], robotics or servo systems [6], moreover they are common choice in electric vehicles [7], [8].

A Fractional-Order ADRC Architecture for a PMSM Position Servo System with Improved Disturbance Rejection

“…The current reference of the d-axis i * d = 0 is used to decouple the stator voltages and currents. Thus, the voltage equation represented by (20) is simplified to…”

“…Ref. [20] applied the ADRC with a model-aided ESO (MESO) in the current control of a PMSM, obtaining enhanced parameter robustness performance. Some studies have modified the structure of ADRC to improve the control performance.…”

“…(3) Simulation and experimental comparison tests on a PMSM position servo system were conducted to verify the effectiveness of the proposed method. Linear GESO Fractional-Order PD [20] Linear Model-Aided ESO Model Predictive Control [25] Nonlinear Nonlinear ESO Fractional-order PD [29] Linear LESO Fractional-Order PD [26,27] Linear FOESO PID [28] Linear FOESO Fuzzy Self-Tuning PD…”

Robustness Enhanced Predictive Control for Dual Three Phase PMSM with Duty Ratio Modulation and Parameter-Free Solution

“…Classical predictive control uses predictive models to predict variables for the following instant and determines an optimal vector using a cost function [1]. However, the motors adopt an idealized model that ignores nonlinearity and parameter variations, heavily relying on system accuracy.…”

Design of controllers based on active disturbance rejection control (ADRC) and its integration with model predictive control (MPC)

“…However, these implementations still require an identied nominal model of the process for the state and output observer equations which turns into the standard approach of the disturbance observer-based control. Some recent practical applications of MPC integration with disturbance observers include power electronics [48], [49], motor control [50], [51], [52], autonomous vehicles [53], [54] and process control [55].…”